Title :
Automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of planning CT and FDG-PET/CT images
Author :
Arimura, H. ; Ze Jin ; Shioyama, Yoshiyuki ; Nakamura, Kentaro ; Magome, Taiki ; Sasaki, Motoharu
Author_Institution :
Dept. of Health Sci., Kyushu Univ., Fukuoka, Japan
Abstract :
We have developed an automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of treatment planning computed tomography (CT) and 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/CT images. First, the PET images were registered with the treatment planning CT images through the diagnostic CT images of PET/CT. Second, six voxel-based features including voxel values and magnitudes of image gradient vectors were derived from each voxel in the planning CT and PET /CT image data sets. Finally, lung tumors were extracted by using a support vector machine (SVM), which learned 6 voxel-based features inside and outside each true tumor region determined by radiation oncologists. The results showed that the average DSCs for 3 and 6 features for three cases were 0.744 and 0.899, and thus the SVM may need 6 features to learn the distinguishable characteristics. The proposed method may be useful for assisting treatment planners in delineation of the tumor region.
Keywords :
computerised tomography; gradient methods; learning (artificial intelligence); lung; medical image processing; positron emission tomography; support vector machines; tumours; 18F-fluorodeoxyglucose; FDG-PET/CT images; SVM; diagnostic CT image; image gradient vector; lung tumor; machine learning classifier; positron emission tomography; radiation oncologist; support vector machine; treatment planning computed tomography; voxel-based feature; Computed tomography; Image segmentation; Lungs; Planning; Positron emission tomography; Support vector machines; Tumors;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6610168